| import os |
| from mmengine.dist import master_only |
| from vlm.datasets.evaluation.base_eval_dataset import BaseEvalDataset |
|
|
| class LLaVAPretrainDataset(BaseEvalDataset): |
| METAINFO: dict = dict(name='llava_pretrain') |
| def __init__( |
| self, |
| image_folder, |
| split=8, |
| rank=0, |
| ): |
| super().__init__() |
| self.image_files = os.listdir(image_folder) |
| |
| self.image_paths = self._get_image_paths(image_folder) |
|
|
| assert rank < split |
| size = len(self.image_paths) // split + 1 |
| i_s, i_e = size * rank, min(size * (rank + 1), len(self.image_paths)) |
| self.image_paths = self.image_paths[i_s: i_e] |
|
|
| def _get_image_paths(self, folder_path): |
| ret = [] |
| sub_files = os.listdir(folder_path) |
| for file_name in sub_files: |
| if '.png' not in file_name and '.jpg' not in file_name: |
| |
| sub_folder_files = self._get_image_paths(os.path.join(folder_path, file_name)) |
| ret.extend(sub_folder_files) |
| else: |
| ret.append(os.path.join(folder_path, file_name)) |
| return ret |
|
|
| def __len__(self): |
| return len(self.image_paths) |
|
|
| def __getitem__(self, idx): |
| data_dict = {'image_path': self.image_paths[idx]} |
| data_dict.update({'text_prompts': self.image_paths[idx], 'pixel_values': None, 'img_id': str(idx)}) |
| return data_dict |
|
|
| @master_only |
| def evaluate(self, **kwargs): |
| return {'Acc': 0} |
|
|
